code: def insight_1_feature_imp(self, df, classes=[0, 1]):
      top_important_variables = []
      average_shap_values = []
      df_numpy = df.to_numpy()
  
      for i in range(3):
          top_important_variables.append(df_numpy[-1 * (i + 1)][0])
          average_shap_values.append(df_numpy[-1 * (i + 1)][1])
  
      sentences = []
      sentences.append(
          "The top three features influencing the models predicted outcome are: {}, {}. {}".format(
              str(top_important_variables[0]),
              str(top_important_variables[1]),
              str(top_important_variables[2]),
          )
      )
  
      # sentences.append("According to your model,  " + str(top_important_variables[0]) + " , " +
      #                  str(top_important_variables[1]) + " , " +
      #                  str(top_important_variables[2]) + " " + "are the top three most important variables.")
  
      return sentences
  
file_path: explainx/lib/insight_classification.py
imports: from imports import *
label_desc: Use image node_id 2 for calling the insight_classification obj's underlying member method code with example usage: obj.insight_1_feature_imp(df, classes) and returns: sentences